Security in Internet of Drones: A Comprehensive Review

Figures & data

Figure 1. World’s first passenger drone “manned drone” 184 (Whittle, 2017).

Table 1. Comparison of the current survey with previous related surveys/reviews/studies

Reference	Analysis type	Publication year	Areas explored during the analysis
(Krishna & Murphy, 2017)	Review	2017	Cybersecurity Vulnerabilities for IoD networks
(Ebeid et al., 2018)	Survey	2018	Open-Source Hardware flight controllers, open-source software flight controllers for IoD networks, and open-source simulation platforms for UAVs
(Syed et al., 2021)	Survey	2021	Security of UAV applications by using Machine Learning, Blockchain, and Watermarking Techniques
(Boccadoro et al., 2021)	Survey	2021	Applications of IoD networks for each layer of Internet Protocol Stack, Security for UAV application-specific scenarios, Authentication and Security Frameworks for IoD networks, and security vulnerabilities in IoD networks from the perspective of Physical layer
(A. Sharma et al., 2020)	Survey	2020	Machine learning, Path Planning, Optimization, Encryption techniques for secure and efficient drone communications; Applications of IoD communications
(Yaacoub et al., 2020)	Survey	2020	Application domains of UAVs, Security vulnerabilities in UAV networks, and their corresponding countermeasures and detection mechanisms
(Zhi et al., 2020)	Survey	2020	Analysis of IoD network safety and vulnerabilities in terms of 3 aspects: sensors, UAV communications, and Multi-UAV networks
(Altawy & Youssef, 2016)	Survey	2017	Physical and Cyberthreats on civilian IoD networks, and the corresponding security measures against such threats
(Hentati et al., 2018)	Study	2018	Suitable UAV simulators, and GCS software frameworks
(Ranyal & Jain, 2021)	Review	2021	GPS spoofing attacks on IoD networks, and the corresponding security measures against such attacks
Our Review	Review	—	Aerial Informatics and Robotics Simulation (AirSim) simulations for UAV tasks and applications; Security of IoD networks using key agreement frameworks, authentication frameworks, blockchain mechanisms, and encryption algorithms

Figure 2. Overview of the review paper.

Table 2. Total number of papers referred for each section of the review paper

Section	Number of papers
Introduction	33
UAV Architecture & UAV Types	21
UAV Regulations, and UAV Communication Protocol	3
GCS software, UAV simulators and UAV software frameworks	14
Review on UAV applications simulations using AirSim simulator	24
Review on secure communication in IoD networks	99
Attack or threat models for UAV communication, and Attacks on Drones	7
Security analysis tools or models	13

Figure 3. Components of a UAV system (Pastor et al., 2006).

Figure 4. Taxonomy of UAV types.

Figure 5. Static drones (Albalawi & song, 2019).

Figure 6. Dynamic drones (Albalawi & song, 2019).

Figure 7. Hybrid drones (Albalawi & song, 2019).

Figure 8. Top view of a hexacopter (Verbeke et al., 2014).

Figure 9. Schematic view of a Titan-based fixed-wing drone (Pellerito et al., 2020).

Figure 10. Parrot AR drone power edition 2.0 (Bouafif et al., 2020).

Figure 11. DJI Phantom 3 (Suroso & Irmawan, 2019).

Table 3. Classification of UAVs based on flying range and single flight time

Category	Flying range	Single flight time
Micro UAV	$<$10 km	$<$1 h
Mini UAV	$<$10 km	1–2 h
Close Range UAV	10–30 km	2–3 h
Short Range UAV	30–70 km	3–6 h
HALE UAV	$>$2000 km	24–48 h

Figure 12. Taxonomy for Section 4 .

Table 4. Short review on AirSim simulations for UAV application experiments

Ref	RL based?	SE or SRE?	AirSim environment(s)	Features
(Zafar et al., 2021)	No	SE	3D AirSim with flood images on its floor	• Components: Monitoring, Communications, Network management, and Realignment. • The flight controller module is used in AirSim for programmatic drone movement and MAVLink protocol for communication with the simulated drones. • Different delivery tasks like food delivery, water delivery, clothes delivery, life saving, and other undefined class of tasks are done for evaluation. • realignment component has a completion rate of 69% and a processing time of 12 min for 2600 available tasks, which shows a 14% increased completion rate and reduction of 20 min in processing time when compared to that of a centralized system for drones. • Convolutional Neural Network (CNN) model shows around 76% correctly predicted observations accuracy.
(Shamwell et al., 2019)	No	SRE	AirSim MAV	• The proposed method uses multi-scale projections and uses scaling factors of 2, 4, and 8. • Experiments are performed by capturing RGB-D imagery of resolutions 128*480 at 10 Hz. • the translational error percentage and rotational errors of the proposed method are respectively 4.94% and 10.24 which are lesser than the corresponding parameters of VIOLearner (RGB), VIO Learner (no IMU), and Structure From Motion (SFM) Learner approaches.
(Shin et al., 2020)	Yes	SRE	Simulated maze with 4-layered obstacles	• Action Critic network eliminates the need for manual labeling in contrast to the need for manual labeling in conventional supervised learning, by extracting depth map from a given environment, and it provides flexibility to a potential change in the environment. • AirSim simulations are done on UBUNTU 16.04 OS, with programming done using Python 3.6. • Action Critic using Kronecker-Factored Trust region (ACKTR) actor-network has the best performance in simulated training environment when compared to 3 other actor-critic algorithms. • Proposed network provides effective avoidance of obstacles, even with progressive changes in the real environment.
(J. Li et al., 2019)	No	SRE	Urban highway	• The traffic dataset generation for vehicles detection is done by capturing imaging using UAV DJI-MATRICE 100 with image resolutions 1280*960. • For simulation setup, 2 devices are used: client computer for building a simulation environment and generating output scene images, server computer acts as a processor for implementation of the algorithm and obtaining speed estimation result of a vehicle. • Speed estimation of a vehicle is done using 6 different conditions of monitoring done by UAV (i.e 6 different case scenarios), by considering directions, uniform and variable motions, and observation heights of UAV. • While considering the 6 case scenarios of simulation, the average measurement error is around 1.275 m/s.
(Arce et al., 2020)	No	SRE	Neighborhood, Refinery, and Landscape Mountains	• The proposed method is used for terrains mapping without Digital Elevation Model (DEM)s or with incomplete DEMs. • For the field study, a municipal water tank is chosen DJI Phantom 4 UAV and a computer with Windows 64-bit OS and 15.86GB RAM are used for field study. • In the simulations, the obtained errors are directly proportional to the complexity of the chosen environment, hence the refinery environment produces more errors. • Validation of iterative modeling for the AirSim simulation environment is done at 3.4 cm per pixel, and that for the field study case is done at less than 3 cm per pixel. • A field study case can just use few iterations to successfully map an unknown area.
(Deng et al., 2020)	No	SRE	Urban Environment	• Real data is collected by using commercial DJI Matrice 100 quadcopter. • Simulations use a monocular camera resolution of 320*240, and a field of view set to 53. • simulation shows that the proposed method gives an accurate and smooth velocity estimate.
(Shin et al., 2019)	Yes	SE	Woodlands, Arena, and Blocks World	• The experiments are performed using simple flight configuration in the settings.json configuration file for AirSim, and Python 3.6 on Ubuntu 16.04 OS computer. • Training for discrete action space shows that a depth map provides more information for learning than by RGB, and that for continuous action space shows that drones make successful paths by avoiding obstacles. • The racing experiment shows that the Arena environment is tougher than the other 2 for racing and that maneuvering through 3D obstacles can be done most probably by trained pilots.
(Koutras et al., 2020)	No	SE	City Environment	• The paper uses a unit called time step which is the time interval at which a UAV can change its position and orientation. • Based on RGB images obtained by the UAVs, the YOLOv3 detector is used for the calculation of the detected objects. • Performance analysis shows that the proposed algorithm achieves a performance that is equivalent to that from 60 different UAVs’ configurations in a semi exhaustive algorithm to decide upon the next monitoring positions of UAVs.
(Goh et al., 2021)	Yes (DRL)	SE	Air Drone Rescue environment (+ 7 additional environments like City, Coastline, Forest, Africa, Football Field, Neighborhood, and Plains)	• In the proposed algorithm, the AirSim simulations are done by configuring 4 drones of which one is a target drone and the other 3 are follower drones. • The proposed algorithm also uses a metric called reward function which ensures the level to which the follower drones keep up their distances and altitude with the target drone, and their formation maintenance while moving with the target drone. • Experimental analysis shows that in the first iteration, Deep Recurrent Q-Network (DRQN) with a triple model achieves a higher reward than DRQN with a single model. During the third iteration, DRQN with a single model achieves effectiveness of 66%.
(Wei et al., 2019)	Yes	SE	Room of size 20 m*20 m	• The environment resets if the UAV crashes or if the distance between the UAV and the target is less than 1 m. • During the implementation, around 50,000 steps are trained. • The results show that the proposed method has a success rate of 98.9% which is extremely high when compared to that of the other 2 detection and recognition methods.
(Bondi et al., 2020)	No	SRE	Africa Savanna environment	• The proposed method uses 62,000 real frames and 1lakh synthetic frames. • The dataset performs tasks like detection, single-object tracking, and multi-object tracking. • The real data and synthetic data have significant variations in terms of content and artifacts. • State of art detection models perform well for large animals, but performance declines when the animals are small.
(S. Wang et al., 2017)	No	SRE	Landscape Mountains	• The Virtual Reality (VR) platform is used for displaying different objects like mountains, trees, and lakes, and all 4 seasonal changes. • UAV VR platform uses UE4 engine since UAV deep learning needs an accurate presentation of even minute things like lake reflections, shadows, and trees’ elastic deformations. • During a manual or automated AirSim simulation, images and data can be obtained for the process of UAV deep learning.
(Hsieh et al., 2019)	No	SRE	Not specified	• Karlsruhe Institute of Technology and Toyota Technological Institute (KITTI) dataset is used in addition to AirSim generated dataset. • Performance analysis of the AirSim generated dataset for the proposed model shows that a fully trained model has a precision rate of 82.2% and recall rate of 85.25% which are more than that of corresponding performance metrics of other training settings.
(Zuluaga et al., 2018)	Yes	SE	Neighborhood environment, blocks environment, and a customized environment	• During training, 2 environmental functions are called: Step function, and Reward function. • Performance analysis shows that the success rate of the trained agent is 8% and that of the random agent is 5%.
(Lombard et al., 2020)	No	SE	Blocks environment	• The proposed method does not take into account the speed adaption of a UAV. • During the simulations, drones are placed on the sphere and made to simultaneously cross the sphere with a maximum speed of 3.5 m/s each. • The simulations also show that under extreme conditions, the proposed method cannot ensure safety.
(Baldini et al., 2020)	No	SE	Downtown	• A Deep Neural Network (DNN) architecture for Visual Inertial Odometry (VIO) has been developed for the determination of UAV’s absolute pose and integrated into the closed-loop control system. • The performance analysis of developed architecture for VIO shows that for 6 Degrees of Freedom (df) poses of UAV, the average RMSE is around 0.06 which is the least when compared to that of traditional VIO methods. • The overall system outperforms traditional benchmark EuROC Micro Air Vehicle (MAV) dataset approaches, however sometimes the nth estimated pose is not consistent with (n + 1)^th estimated pose.
(Singh & Alvarez, 2020)	No	SE	Mars simulation environment, and Earth-like environment	• The proposed method has got 3 main entities: Target Acquisition System, Navigation System, and Mars simulation environment. • The major limitation of the proposed method is that the standard water index does not return the desired value (usually above 0.5 for water). The standard water index returns −1 and lower values for non-water objects. • Another disadvantage of the proposed method is that it does not focus on reflectance values, and does not consider the sensor noise.
(Pienroj et al., 2019)	Yes (DRL)	SE	Landscape Mountains with power-line	• Performances of different DRL algorithms are compared on simplified control task which aims for height above the power line. • The Deep Deterministic Policy Gradient (DDPG) algorithm is used for experiments in a more photo-realistic environment that targets simultaneous height control and heading control, which uses independent controllers for height control and heading control. • Overall experiments in the paper consider 3 types of environment: simple, realistic, and lighting. • When policies are adapted to new environments, experiments show that policies of harder environments with additional retraining increase the success rate for adherence to the ideal trajectory; but policies of an easier environment fail to complete the task even once.
(Lindgren et al., 2019)	No	SE	Virtual city environment	• The method is evaluated for the MAV dataset which is collected through the AirSim simulator, as well as against state of art methods based on the KITTI dataset. • During the AirSim simulations, flight speeds in the range 0 to 8 m/s are considered, with 14 trajectories each having a flight time of 108s. • The intensity of fog and snow are kept the same for both the KITTI dataset and the AirSim dataset, but rain intensity being higher for the AirSim dataset. • The proposed method successfully localizes through all environmental conditions and all difficulty levels. • BooM-VIO method outperforms VINS-Mono when different metrics are considered on harsh environments, and different difficulty levels, but does not strictly outperform traditional VIO methods.
(Bondi et al., 2018)	No	SE	Africa Savanna environment	• Since the flights and monitoring are done at night, the built environment is expanded to include thermal infrared imagery for dataset augmentation for the detection of animals and humans. • The emissivity of the objects in the environment varies between 0.8 and 0.99 on average for different objects in the environment. • The environment also considers a parameter called Point Spread Function (PSF) which measures the blurriness of the image, and also noise in the image. • Creation of environment and generation of simulation data took around 200hrs, and real data labeling took around 800hrs. • AirSim-W is a potential environment for a cost-effective solution for wildlife conservation.
(Mazdin & Rinner, 2019)	No	SE	Environment measuring an area of 240 m*240 m, which has building blocks of different sizes of interest, and a GCS at the center of the area	• The default flight controller SimpleFlight is used for drones, and the recent ROS2 version is incorporated for a fully decentralized system. • Exploration time is independent of the number of drones but depends on environment uncertainty and exploration method.
(Vemprala & Saripalli, 2018)	No	SE	Sparsely populated environment, and Urban environment	• The proposed algorithm handles the problem of pure rotation movement, by feature points matching between rotating MAV and another MAV which is used to observe common scene points. • When the proposed algorithm is applied to a sparsely populated environment, inter-MAV localization provides higher robustness when compared to intra-MAV localization. • Fusion of inter-MAV and intra-MAV localization with occasional fusions and relative measurements demonstrates the improvement in the overall localization accuracy.

Figure 13. Taxonomy for Section 6 .

Table 5. Resistance of the key exchange mechanisms/frameworks against some attacks

Resistance against attacks	(Putranto et al., 2019)	(Gope & Sikdar, 2020)	(Tanveer et al., 2020)	(Y. Zhang et al., 2020)	(Sahingoz, 2013)	(Yahuza et al., 2021)	(Khan et al., 2021)	(Tanveer et al., 2021)
Denial-of-Service (DoS)	—	—	✓	—	—	✓	✓	✓
DDoS	✓	—	—	—	—	—	—	—
Physical	—	✓	—	—	—	—	—	—
Location threat	—	✓	—	—	—	—	—	—
Impersonation	—	✓	✓	✓	—	✓	✓	✓
Replay	—	✓	✓	✓	—	✓	✓	✓
Modification	—	—	—	✓	—	—	—	—
Eavesdropping	✓	✓	—	—	—	—	—	—
Offline password guessing	—	—	✓	—	—	—	—	✓
Online password guessing	—	—	—	—	—	—	—	✓
Password and bio-metric update	—	—	✓	—	—	—	—	—
Drone capture	—	—	✓	✓	—	—	—	✓
Identity guessing	—	—	✓	—	—	—	—	✓
MITM	—	—	✓	✓	—	✓	✓	✓
Server spoofing	—	—	—	✓	—	—	—	—
Stolen smart device/card	—	—	✓	✓	—	—	—	✓
Node capture	—	—	—	—	✓	—	—	—
Key escrow	—	—	—	—	—	✓	—	—
Known session key	—	—	—	✓	—	—	—	—
Known session -specific temporary information	—	—	—	—	—	✓	—	—
Private key leakage	—	—	—	—	—	✓	—	—
Perfect forward secrecy	—	—	—	—	—	✓	—	—
Malicious device deployment	—	—	—	—	—	—	✓	—
Cloning	—	—	—	—	—	—	✓	—
Privileged insider	—	—	✓	—	—		—	✓
Ephemeral Secret Leakage (ESL)	—	—	✓	—	—	—	✓	✓
De-synchronization	—	—	—	—	—	—	✓	✓
Hijacking	✓	—	—	—	—	—	—	—
Forgery	—	—		—	—	—	—	—

Table 6. Resistance of authentication mechanisms/frameworks against some attacks

Resistance against attacks	(Nikooghadam etal., 2020)	(Ever, 2020)	(Gope et al., 2021)	(Y. Li et al., 2020)	(Alladi, Naren, et al., 2020)	(Pu & Li, 2020)	(Alladi, Chamola, et al., 2020)	(Abdel-Malek et al., 2020)	(X. Xu et al., 2019)	(Hong et al., 2020)	(C.-L. Chen et al., 2020)	(G. Cho et al., 2020)	(Pirker et al., 2020)	(Srinivas et al., 2019)	(Wazid et al., 2018)	(Ali et al., 2020)	(Lei et al., 2021)	(Chaudhry et al., 2021)	(Hussain et al., 2021)	(Jan et al., 2021)
Impersonation	✓	✓	—	—	—	—	✓	—	✓	—	—	✓	—	✓	✓	✓	✓	✓	✓	✓
Verifier	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Drone capture	✓	—	—	—	—	—	—	—	—	—	—	—	—	✓	✓	✓	—	✓	✓	✓
Insider	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
MITM	✓	—	—		✓	✓	✓	—	—	—	—	✓	—	✓	✓	✓	✓	✓	✓	✓
Replay	✓	✓	✓	✓	✓	✓	✓	✓	—	✓	—	✓	—	✓	✓	✓	✓	✓	✓	✓
Password guessing	—	✓	—	—	—	—	—	—	—	—	—	—	—	✓	✓	✓	—	—	✓	-
Wi-Fi	—	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Traceability	—	—	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	✓
DoS	—	—	✓	—	—	—	—	—	—	—	✓	—	—	✓	✓	✓	✓	—	✓	✓
Physical	—	—	✓	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	—	-
De-synchronization	—	—	✓	—	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Message modification	—	—	—	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Masquerade	—	—	—	—	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	✓
Tampering	—	—	—	—	✓	✓	✓	—	—	—	—	—	—	—	—	—	—	—	—	—
Cloning	—	—		—	✓	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Coalition	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	—	—	—	—	-
Spoofing	—	✓	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	—	—	—	✓
Known session key	✓	—	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	—	—	-
Remote	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	—	-
Bio-metric update	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	-
ESL	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	✓	—	✓	—	—
Privileged insider	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	✓	✓	—	✓	✓	✓
Identity guessing	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	—	—	—	-
Asynchronous	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	-
Brute force	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	✓	—	—	-
Stolen verifier	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	✓
Session-specific temporary information	✓	—	✓	✓	—	—	—	—	—	—	—	—	—	—	—	—	—	—	—	-
Node capture	—	✓	✓	✓	—	—	—	—	—	—	—	—	—	✓	✓	✓	—	—	—	—

Table 7. Resistance of the IoD communication blockchain methods against some attacks

Resistance to attacks	(Wazid, Das, et al., 2020)	(Y. Tan et al., 2020)	(Bera, Saha, et al., 2020)	(Bera, Das, Sutrala, et al., 2021)	(Bera, Chattaraj, et al., 2020)	(Wazid, Bera, et al., 2020)	(W. Xiao et al., 2021)
MITM	✓	—	✓	✓	✓	✓	—
Impersonation	✓	✓	✓	✓	✓	✓	—
Password guessing	✓	—	—	—	—	✓	—
Data modification	✓	—	—	—	—	✓	—
Data leakage	✓	—	—	—	—	✓	—
Eavesdropping	—	✓	—	—	—	—	—
Tampering	—	✓	—	—	—	—	—
Replay	✓	✓	✓	✓	✓	✓	—
Cloning	—	✓	—	—	—	—	—
Privileged insider	✓	—	✓	✓	✓	—	—
Malicious device deployment attack	—	—	✓	✓	✓	—	—
Drone/device physical capture attack	✓	—	✓	✓	✓	—	—
DoS	—	—	—	—	—	✓	—
Double-spend	—	—	—	—	—	—	✓
Illegal session key computation	✓	—	—	—	—	—	—
Internal	—	✓	—	—	—	—	—
Compromise	—	✓	—	—	—	—	—
ESL	✓	—	✓	✓	✓	—	-

Table 8. Details of some security analysis models and logics used for analysis of IoD communication

Ref	Model or logic	Authors	Features
(Ouafi & Phan, 2008)	Ouafi and Phan model	Khaled Ouafi and Raphael C W Phan	• Used for formal security analysis of RFID-based authentication protocols. • Used to verify tag anonymity and tag untraceability properties. • An adversary is capable of issuing the following queries in the communication network: Execute, Send, Corrupt, and Test.
(Bellare & Rogaway, 1993)	Random Oracle Model	Mihir Bellare and Phillip Rogaway	• Provides a bridge between cryptography practice and cryptography theory. • Replaces the random oracle with an object like a hash function. • Candidate instantiations use hash functions in different ways: truncated hash output, restricted input length of hash functions, and hash functions used in a non-standardized manner.
(Burrows et al., 1989) (Boyd & Mao, 1993)	BAN logic	Michael Burrows, Martin Abadi, and Roger Needham	• Differentiates different types of objects or statements like principals, encryption keys, and formulas. • Uses statements for the identification of messages. • Considers different properties for each protocol: protocol goal, whether secret keys are used or not, type of cryptosystem used, redundancy, bugs, whether or not the protocol proves the presence of each participating entity to the other, and whether timeliness of the messages are guaranteed with nonces or synchronized clocks. • BAN logic also has some limitations: incapability to express some events, lack of precision in movement from a protocol description to logical expression, misjudgment of some insecure protocols to be secure.
(Mao & Boyd, 1993)	Mao Boyd logic	Wembo Mao and Colin Boyd	• Adopts BAN logic’s basic notation framework, and a more formalized approach. • Has few rules for formalizing authentication and confidentiality scenarios: authentication rules, confidentiality rules, nonce verification rule, the super principal rule, the fresh rule, the good key rules, and intuitive rules.
(Gong et al., 1990)	GYN logic	Li Gong, Roger Needham, and Raphael Yahalom	• Provides a systematic way of understanding the cryptography protocols. • does not need several universal assumptions as that of BAN logic. • differentiates between the content of a message and the information implication of the message. • Regards each protocol as a distributed algorithm.

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